optimising fuzzy neural network architecture for dissolved oxygen prediction and risk analysis

Abstract

A fuzzy neural network method is proposed to predict minimum daily dissolved oxygen concentration in the Bow River, in Calgary, Canada. Owing to the highly complex and uncertain physical system, a data-driven and fuzzy number based approach is preferred over traditional approaches. The inputs to the model are abiotic factors, namely water temperature and flow rate. An approach to select the optimum architecture of the neural network is proposed. The total uncertainty of the system is captured in the fuzzy numbers weights and biases of the neural network. Model predictions are compared to the traditional, non-fuzzy approach, which shows that the proposed method captures more low DO events. Model output is then used to quantify the risk of low DO for different conditions.